Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	2157
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	255.1 KiB
Average record size in memory	121.1 B

Variable types

DateTime	1
Categorical	5
Numeric	10

Warnings

`username` has constant value "elonmusk"	Constant
`cashtags` has constant value "0"	Constant
`tweet` has a high cardinality: 2157 distinct values	High cardinality
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`video` is highly correlated with `photos` and 1 other fields	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 2 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 2 other fields	High correlation
`likes_count` is highly correlated with `video` and 3 other fields	High correlation
`number of tweets` is highly correlated with `replies_count` and 2 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`photos` is highly correlated with `video`	High correlation
`bins` is highly correlated with `percent change`	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`percent change` is highly correlated with `bins`	High correlation
`hashtags` is highly correlated with `cashtags` and 1 other fields	High correlation
`cashtags` is highly correlated with `hashtags` and 2 other fields	High correlation
`bins` is highly correlated with `cashtags` and 1 other fields	High correlation
`username` is highly correlated with `hashtags` and 2 other fields	High correlation
`tweet` is uniformly distributed	Uniform
`date` has unique values	Unique
`tweet` has unique values	Unique
`mentions` has 1944 (90.1%) zeros	Zeros
`video` has 1668 (77.3%) zeros	Zeros
`photos` has 1706 (79.1%) zeros	Zeros
`urls` has 1609 (74.6%) zeros	Zeros

Reproduction

Analysis started	2021-09-27 19:02:25.376573
Analysis finished	2021-09-27 19:02:43.719925
Duration	18.34 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

date
Date

UNIQUE

Distinct	2157
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	17.0 KiB

Minimum	2016-08-23 16:00:00
Maximum	2021-07-20 09:30:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	2157
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	17.0 KiB

Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	1
@KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ	1
@alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg	1
@flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times.	1
This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg	1
Other values (2152)	2152

Length

Max length	6926
Median length	274
Mean length	475.8701901
Min length	1

Characters and Unicode

Total characters	1026452
Distinct characters	343
Distinct categories	18 ?
Distinct scripts	7 ?
Distinct blocks	16 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2157 ?
Unique (%)	100.0%

Sample

1st row	Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
2nd row	@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.
3rd row	Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY
4th row	@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh
5th row	Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.

Common Values

Value	Count	Frequency (%)
Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	1	< 0.1%
@KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ	1	< 0.1%
@alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg	1	< 0.1%
@flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times.	1	< 0.1%
This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg	1	< 0.1%
@JBNielsen1985 @stephenpallotta @ajtourville @Teslarati Both @InSpaceXItrust @Kristennetten @thirdrowtesla Could maybe tap the condensation for water too. Seems odd that HVAC systems make pure, fresh water & just dump it on the ground. @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Thanks Tesla China team, China Customs Authority & LAX customs for acting so swiftly @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Yup, China had an oversupply, so we bought 1255 FDA-approved ResMed, Philips & Medtronic ventilators on Friday night & airshipped them to LA. If you want a free ventilator installed, please let us know! @Kristennetten @thirdrowtesla Yeah, pretty much. House could talk to car & know when you’re expected home, so temp & humidity would be perfect just as you arrive. No wasted energy. @romn8tr @stephenpallotta @ajtourville @Teslarati This is a way bigger deal than most people realize @stephenpallotta @ajtourville @Teslarati Sure would love to do home hvac that’s quiet & efficient, with humidity control & HEPA filter @stephenpallotta @ajtourville @Teslarati Yes. PCB design techniques applied to create a heat exchanger that is physically impossible by normal means. Heat pump also has a local heating loop to spool up fast & extend usable temperature range. Octavalve is pretty special too. Team did great work. No credit to me. @ajtourville @Teslarati The heat pump and rear body castings are a step beyond @Teslarati Model Y heat pump is some of the best engineering I’ve seen in a while. Team did next-level work. @justpaulinelol @engineers_feed 🤣🤣 @BocachicaMaria1 Will do @teslaownersSV @RenataKonkoly @sdunbabin @jonkay @Quillette Most likely, imo, we will see a significant reduction in the confirmed C19 case growth rate this week. Follow chart at bottom of this CDC page: https://t.co/vZ3PbhQihG @RenataKonkoly @sdunbabin @jonkay @Quillette Exactly @jonkay @Quillette According to the Italian govt, only 12% of deaths are actually due to C19. This is a significant policy difference in Italy vs most other countries. @engineers_feed Engineering ftw @flyLAXairport Much appreciated	1	< 0.1%
@PPathole @BBCScienceNews Have you seen more data on HCQ & Z-Pak? Hard to find. @BBCScienceNews Worrisome @thirdrowtesla Yup @JohnnaCrider1 @thirdrowtesla We’ll try to get & deliver as many as possible. N95 masks are a pain to wear btw. Less onerous masks are better most of the time. @Kristennetten @thirdrowtesla @SalehCU Yeah. We have a mask shipment stuck at LAX. Hopefully freed up soon. @engineers_feed Nice @JohnCleese This is great advice @uwdrwaldorf @Tesla @omead_a @UWMedicine Thanks for taking delivery in your garage! Let us know if there’s anything else you need. @justpaulinelol @EvaFoxU @Tesla We expect to have over ~1200 to distribute this week. Getting them delivered, installed & operating is the harder part. @EvaFoxU @Tesla Supply chain logistics — getting masks & other PPE to the right places in time — is the main issue we’re hearing from ER physicians @NativeCACV @Tesla @omead_a You’re most welcome. We’re working on getting other types of PPE too. Ventilators should arrive within a few days.	1	< 0.1%
@benikbeno @CDCgov Panic is always dumb @Guruleaks1 @benikbeno Italy: “On re-evaluation by the National Institute of Health, only 12 per cent of death certificates have shown a direct causality from coronavirus.” @CDCgov Close contact family gatherings that mix young kids, who have almost no risk of C19 death, with grandparents who have high risk (especially those with prior lung damage), are one of the most powerful mortality vectors	1	< 0.1%
@Jennerator211 We have N95 masks & getting PAPRs. Will have our team reach out.	1	< 0.1%
Just had a long engineering discussion with Medtronic about state-of-the-art ventilators. Very impressive team! @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 No problem :) @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 Sigmoid (S-curve) is how all physical and mental viruses behave, regardless of containment. Containment reduces asymptote of S-curve. At this point, we have strict containment in US/Europe & should expect similarly reduced asymptote to China. @flcnhvy @thirdrowtesla @NateSilver538 Sigmoidal for China, followed by sigmoidal for rest of world @Lauren62515251 @PPathole @NateSilver538 Fair point :) @RiccitelliDylan @ColdBuschLights @PPathole @NateSilver538 Exactly, both would be false positive. Also, dying with C19 is different from dying because of C19. Vast majority who died had other illnesses too. https://t.co/YKMW54q2kL @PPathole @NateSilver538 Up to 80% false positive @PPathole @NateSilver538 Sure, although ventilator companies definitely know how to make ventilators. Just a spike in demand right now. Also, using CPAP machines for less severe cases & using one ventilator for several patients seem like good moves to meet short-term demand. @NateSilver538 Do you have data on the false positive rate? I’ve heard a wide range of numbers. CDC numbers are almost an order of magnitude lower between high fidelity data (onset date known) and “presumed positive”. https://t.co/vZ3PbhQihG @NateSilver538 Important consideration	1	< 0.1%
Other values (2147)	2147	99.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
to	3519	2.3%
the	3178	2.1%
is	2759	1.8%
a	2718	1.8%
of	2336	1.5%
amp	1945	1.3%
in	1829	1.2%
for	1593	1.0%
tesla	1585	1.0%
will	1328	0.9%
Other values (18820)	130347	85.1%

Most occurring characters

Value	Count	Frequency (%)
	152955	14.9%
e	85937	8.4%
a	65732	6.4%
t	65477	6.4%
o	58861	5.7%
i	51540	5.0%
s	49688	4.8%
r	49202	4.8%
n	48414	4.7%
l	39327	3.8%
Other values (333)	359319	35.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	747582	72.8%
Space Separator	152955	14.9%
Uppercase Letter	56415	5.5%
Other Punctuation	46165	4.5%
Decimal Number	14404	1.4%
Final Punctuation	2329	0.2%
Connector Punctuation	2152	0.2%
Other Symbol	1459	0.1%
Dash Punctuation	870	0.1%
Close Punctuation	575	0.1%
Other values (8)	1546	0.2%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
🤣	424	29.1%
♥	122	8.4%
🔥	58	4.0%
😀	48	3.3%
🖤	41	2.8%
🚀	38	2.6%
😉	33	2.3%
💕	29	2.0%
👍	24	1.6%
🚘	17	1.2%
Other values (192)	625	42.8%

Lowercase Letter

Value	Count	Frequency (%)
e	85937	11.5%
a	65732	8.8%
t	65477	8.8%
o	58861	7.9%
i	51540	6.9%
s	49688	6.6%
r	49202	6.6%
n	48414	6.5%
l	39327	5.3%
h	29311	3.9%
Other values (41)	204093	27.3%

Uppercase Letter

Value	Count	Frequency (%)
S	6017	10.7%
T	5996	10.6%
A	3718	6.6%
M	3271	5.8%
I	2986	5.3%
C	2771	4.9%
E	2603	4.6%
W	2485	4.4%
P	2411	4.3%
B	2129	3.8%
Other values (18)	22028	39.0%

Other Punctuation

Value	Count	Frequency (%)
@	17453	37.8%
.	9613	20.8%
,	5422	11.7%
/	4858	10.5%
;	2039	4.4%
&	2034	4.4%
:	1618	3.5%
!	1483	3.2%
?	394	0.9%
…	350	0.8%
Other values (5)	901	2.0%

Decimal Number

Value	Count	Frequency (%)
0	2740	19.0%
1	2412	16.7%
2	1812	12.6%
3	1766	12.3%
5	1179	8.2%
4	1003	7.0%
8	938	6.5%
9	936	6.5%
7	915	6.4%
6	703	4.9%

Math Symbol

Value	Count	Frequency (%)
~	273	70.4%
+	97	25.0%
=	12	3.1%
∩	2	0.5%
≥	2	0.5%
√	1	0.3%
∆	1	0.3%

Other Letter

Value	Count	Frequency (%)
中	1	14.3%
南	1	14.3%
海	1	14.3%
紫	1	14.3%
光	1	14.3%
阁	1	14.3%
𖨆	1	14.3%

Open Punctuation

Value	Count	Frequency (%)
(	514	98.5%
[	7	1.3%
{	1	0.2%

Close Punctuation

Value	Count	Frequency (%)
)	567	98.6%
]	6	1.0%
}	2	0.3%

Dash Punctuation

Value	Count	Frequency (%)
-	798	91.7%
—	47	5.4%
–	25	2.9%

Currency Symbol

Value	Count	Frequency (%)
$	166	98.8%
€	1	0.6%
£	1	0.6%

Initial Punctuation

Value	Count	Frequency (%)
“	239	97.2%
‘	7	2.8%

Final Punctuation

Value	Count	Frequency (%)
’	2087	89.6%
”	242	10.4%

Format

Value	Count	Frequency (%)
‍	19	95.0%
	1	5.0%

Modifier Symbol

Value	Count	Frequency (%)
^	10	90.9%
🏻	1	9.1%

Space Separator

Value	Count	Frequency (%)
	152955	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	2152	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	184	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	803951	78.3%
Common	222247	21.7%
Inherited	203	< 0.1%
Cyrillic	39	< 0.1%
Han	6	< 0.1%
Greek	5	< 0.1%
Bamum	1	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	152955	68.8%
@	17453	7.9%
.	9613	4.3%
,	5422	2.4%
/	4858	2.2%
0	2740	1.2%
1	2412	1.1%
_	2152	1.0%
’	2087	0.9%
;	2039	0.9%
Other values (246)	20516	9.2%

Latin

Value	Count	Frequency (%)
e	85937	10.7%
a	65732	8.2%
t	65477	8.1%
o	58861	7.3%
i	51540	6.4%
s	49688	6.2%
r	49202	6.1%
n	48414	6.0%
l	39327	4.9%
h	29311	3.6%
Other values (48)	260462	32.4%

Cyrillic

Value	Count	Frequency (%)
о	9	23.1%
в	4	10.3%
К	3	7.7%
р	3	7.7%
л	3	7.7%
д	2	5.1%
к	2	5.1%
ё	2	5.1%
и	2	5.1%
а	1	2.6%
Other values (8)	8	20.5%

Han

Value	Count	Frequency (%)
中	1	16.7%
南	1	16.7%
海	1	16.7%
紫	1	16.7%
光	1	16.7%
阁	1	16.7%

Inherited

Value	Count	Frequency (%)
️	184	90.6%
‍	19	9.4%

Greek

Value	Count	Frequency (%)
Δ	4	80.0%
θ	1	20.0%

Bamum

Value	Count	Frequency (%)
𖨆	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1021709	99.5%
Punctuation	3017	0.3%
None	1045	0.1%
VS	184	< 0.1%
Emoticons	162	< 0.1%
Misc Symbols	161	< 0.1%
Enclosed Alphanum Sup	70	< 0.1%
Cyrillic	39	< 0.1%
Dingbats	25	< 0.1%
Latin 1 Sup	19	< 0.1%
Other values (6)	21	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	152955	15.0%
e	85937	8.4%
a	65732	6.4%
t	65477	6.4%
o	58861	5.8%
i	51540	5.0%
s	49688	4.9%
r	49202	4.8%
n	48414	4.7%
l	39327	3.8%
Other values (80)	354576	34.7%

Punctuation

Value	Count	Frequency (%)
’	2087	69.2%
…	350	11.6%
”	242	8.0%
“	239	7.9%
—	47	1.6%
–	25	0.8%
‍	19	0.6%
‘	7	0.2%
	1	< 0.1%

Dingbats

Value	Count	Frequency (%)
❤	16	64.0%
✌	6	24.0%
✨	3	12.0%

VS

Value	Count	Frequency (%)
️	184	100.0%

Emoticons

Value	Count	Frequency (%)
😀	48	29.6%
😉	33	20.4%
😢	9	5.6%
😅	8	4.9%
😍	8	4.9%
😎	6	3.7%
😮	5	3.1%
😜	4	2.5%
🙏	4	2.5%
😔	3	1.9%
Other values (17)	34	21.0%

None

Value	Count	Frequency (%)
🤣	424	40.6%
🔥	58	5.6%
🖤	41	3.9%
🚀	38	3.6%
💕	29	2.8%
👍	24	2.3%
🚘	17	1.6%
💨	17	1.6%
💫	16	1.5%
🛸	16	1.5%
Other values (136)	365	34.9%

Latin 1 Sup

Value	Count	Frequency (%)
é	7	36.8%
ü	6	31.6%
ö	4	21.1%
£	1	5.3%
ä	1	5.3%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇳	11	15.7%
🇴	9	12.9%
🇺	7	10.0%
🇮	7	10.0%
🇸	7	10.0%
🇦	5	7.1%
🇪	5	7.1%
🇯	4	5.7%
🇵	4	5.7%
🇩	4	5.7%
Other values (4)	7	10.0%

Misc Symbols

Value	Count	Frequency (%)
♥	122	75.8%
☺	8	5.0%
♂	8	5.0%
♀	6	3.7%
⚡	6	3.7%
☠	2	1.2%
⚔	2	1.2%
⛺	1	0.6%
⚾	1	0.6%
⛄	1	0.6%
Other values (4)	4	2.5%

Currency Symbols

Value	Count	Frequency (%)
€	1	100.0%

CJK

Value	Count	Frequency (%)
中	1	16.7%
南	1	16.7%
海	1	16.7%
紫	1	16.7%
光	1	16.7%
阁	1	16.7%

Latin Ext A

Value	Count	Frequency (%)
ō	2	66.7%
ē	1	33.3%

Math Operators

Value	Count	Frequency (%)
∩	2	33.3%
≥	2	33.3%
√	1	16.7%
∆	1	16.7%

Letterlike Symbols

Value	Count	Frequency (%)
™	2	50.0%
ℏ	2	50.0%

Cyrillic

Value	Count	Frequency (%)
о	9	23.1%
в	4	10.3%
К	3	7.7%
р	3	7.7%
л	3	7.7%
д	2	5.1%
к	2	5.1%
ё	2	5.1%
и	2	5.1%
а	1	2.6%
Other values (8)	8	20.5%

Bamum Sup

Value	Count	Frequency (%)
𖨆	1	100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.0 KiB

elonmusk	2157

Length

Max length	8
Median length	8
Mean length	8
Min length	8

Characters and Unicode

Total characters	17256
Distinct characters	8
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	elonmusk
2nd row	elonmusk
3rd row	elonmusk
4th row	elonmusk
5th row	elonmusk

Common Values

Value	Count	Frequency (%)
elonmusk	2157	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
elonmusk	2157	100.0%

Most occurring characters

Value	Count	Frequency (%)
e	2157	12.5%
l	2157	12.5%
o	2157	12.5%
n	2157	12.5%
m	2157	12.5%
u	2157	12.5%
s	2157	12.5%
k	2157	12.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	17256	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2157	12.5%
l	2157	12.5%
o	2157	12.5%
n	2157	12.5%
m	2157	12.5%
u	2157	12.5%
s	2157	12.5%
k	2157	12.5%

Most occurring scripts

Value	Count	Frequency (%)
Latin	17256	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2157	12.5%
l	2157	12.5%
o	2157	12.5%
n	2157	12.5%
m	2157	12.5%
u	2157	12.5%
s	2157	12.5%
k	2157	12.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	17256	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2157	12.5%
l	2157	12.5%
o	2157	12.5%
n	2157	12.5%
m	2157	12.5%
u	2157	12.5%
s	2157	12.5%
k	2157	12.5%

mentions
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.1325915624

Minimum	0
Maximum	9
Zeros	1944
Zeros (%)	90.1%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	9
Range	9
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.4825713888
Coefficient of variation (CV)	3.639533166
Kurtosis	70.42842177
Mean	0.1325915624
Median Absolute Deviation (MAD)	0
Skewness	6.358881745
Sum	286
Variance	0.2328751453
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	1944	90.1%
1	168	7.8%
2	27	1.3%
3	14	0.6%
4	2	0.1%
9	1	< 0.1%
5	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1944	90.1%
1	168	7.8%
2	27	1.3%
3	14	0.6%
4	2	0.1%
5	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
9	1	< 0.1%
5	1	< 0.1%
4	2	0.1%
3	14	0.6%
2	27	1.3%
1	168	7.8%
0	1944	90.1%

hashtags
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.0 KiB

0	2142
1	14
5	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2157
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2157	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	2157	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2157	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2142	99.3%
1	14	0.6%
5	1	< 0.1%

cashtags
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.0 KiB

0	2157

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2157
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2157	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2157	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	2157	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2157	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2157	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2157	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2157	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2157	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2157	100.0%

video
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3092257765

Minimum	0
Maximum	6
Zeros	1668
Zeros (%)	77.3%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	6
Range	6
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.6612612362
Coefficient of variation (CV)	2.138441509
Kurtosis	9.471674108
Mean	0.3092257765
Median Absolute Deviation (MAD)	0
Skewness	2.704869448
Sum	667
Variance	0.4372664226
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	1668	77.3%
1	357	16.6%
2	99	4.6%
3	23	1.1%
4	8	0.4%
5	1	< 0.1%
6	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1668	77.3%
1	357	16.6%
2	99	4.6%
3	23	1.1%
4	8	0.4%
5	1	< 0.1%
6	1	< 0.1%

Value	Count	Frequency (%)
6	1	< 0.1%
5	1	< 0.1%
4	8	0.4%
3	23	1.1%
2	99	4.6%
1	357	16.6%
0	1668	77.3%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3027352805

Minimum	0
Maximum	7
Zeros	1706
Zeros (%)	79.1%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	7
Range	7
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7049105069
Coefficient of variation (CV)	2.328471613
Kurtosis	15.35249543
Mean	0.3027352805
Median Absolute Deviation (MAD)	0
Skewness	3.297873129
Sum	653
Variance	0.4968988227
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	1706	79.1%
1	314	14.6%
2	97	4.5%
3	25	1.2%
4	10	0.5%
6	3	0.1%
5	1	< 0.1%
7	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1706	79.1%
1	314	14.6%
2	97	4.5%
3	25	1.2%
4	10	0.5%
5	1	< 0.1%
6	3	0.1%
7	1	< 0.1%

Value	Count	Frequency (%)
7	1	< 0.1%
6	3	0.1%
5	1	< 0.1%
4	10	0.5%
3	25	1.2%
2	97	4.5%
1	314	14.6%
0	1706	79.1%

urls
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3597589244

Minimum	0
Maximum	6
Zeros	1609
Zeros (%)	74.6%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	6
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.7356629268
Coefficient of variation (CV)	2.044877491
Kurtosis	10.41605446
Mean	0.3597589244
Median Absolute Deviation (MAD)	0
Skewness	2.768479621
Sum	776
Variance	0.5411999419
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	1609	74.6%
1	391	18.1%
2	110	5.1%
3	32	1.5%
4	9	0.4%
6	3	0.1%
5	3	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1609	74.6%
1	391	18.1%
2	110	5.1%
3	32	1.5%
4	9	0.4%
5	3	0.1%
6	3	0.1%

Value	Count	Frequency (%)
6	3	0.1%
5	3	0.1%
4	9	0.4%
3	32	1.5%
2	110	5.1%
1	391	18.1%
0	1609	74.6%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1706
Distinct (%)	79.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5082.331015

Minimum	6
Maximum	204414
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	6
5-th percentile	67
Q1	407
median	1312
Q3	4015
95-th percentile	22356.4
Maximum	204414
Range	204408
Interquartile range (IQR)	3608

Descriptive statistics

Standard deviation	12495.48218
Coefficient of variation (CV)	2.458612426
Kurtosis	62.6151601
Mean	5082.331015
Median Absolute Deviation (MAD)	1113
Skewness	6.500525765
Sum	10962588
Variance	156137075
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
130	6	0.3%
50	5	0.2%
422	5	0.2%
158	5	0.2%
444	5	0.2%
47	5	0.2%
26	5	0.2%
67	5	0.2%
354	4	0.2%
80	4	0.2%
Other values (1696)	2108	97.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6	1	< 0.1%
9	1	< 0.1%
10	1	< 0.1%
11	1	< 0.1%
12	3	0.1%
13	2	0.1%
18	2	0.1%
19	1	< 0.1%
20	1	< 0.1%
21	2	0.1%

Value	Count	Frequency (%)
204414	1	< 0.1%
151775	1	< 0.1%
144534	1	< 0.1%
117894	1	< 0.1%
111854	1	< 0.1%
104334	1	< 0.1%
102455	1	< 0.1%
89688	1	< 0.1%
88544	1	< 0.1%
86126	1	< 0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1870
Distinct (%)	86.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13350.84979

Minimum	3
Maximum	582467
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	3
5-th percentile	50
Q1	605
median	3044
Q3	12054
95-th percentile	64299
Maximum	582467
Range	582464
Interquartile range (IQR)	11449

Descriptive statistics

Standard deviation	30914.36556
Coefficient of variation (CV)	2.315535419
Kurtosis	77.88037479
Mean	13350.84979
Median Absolute Deviation (MAD)	2835
Skewness	6.726566076
Sum	28797783
Variance	955697998.1
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
41	6	0.3%
76	5	0.2%
16	5	0.2%
23	5	0.2%
157	5	0.2%
18	5	0.2%
235	4	0.2%
29	4	0.2%
56	4	0.2%
28	4	0.2%
Other values (1860)	2110	97.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
3	2	0.1%
4	1	< 0.1%
6	1	< 0.1%
7	2	0.1%
8	2	0.1%
11	1	< 0.1%
12	1	< 0.1%
13	1	< 0.1%
14	4	0.2%
15	1	< 0.1%

Value	Count	Frequency (%)
582467	1	< 0.1%
377180	1	< 0.1%
295702	1	< 0.1%
279902	1	< 0.1%
275873	1	< 0.1%
238014	1	< 0.1%
219620	1	< 0.1%
218913	1	< 0.1%
202697	1	< 0.1%
198533	1	< 0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2136
Distinct (%)	99.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	133849.3426

Minimum	95
Maximum	4727301
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	95
5-th percentile	1099
Q1	11054
median	41308
Q3	138600
95-th percentile	602881.4
Maximum	4727301
Range	4727206
Interquartile range (IQR)	127546

Descriptive statistics

Standard deviation	253783.9651
Coefficient of variation (CV)	1.896041924
Kurtosis	59.63098605
Mean	133849.3426
Median Absolute Deviation (MAD)	36566
Skewness	5.416606697
Sum	288713032
Variance	6.440630097 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1099	3	0.1%
264	3	0.1%
608	2	0.1%
2893	2	0.1%
2864	2	0.1%
2398	2	0.1%
9291	2	0.1%
345	2	0.1%
7999	2	0.1%
405	2	0.1%
Other values (2126)	2135	99.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
95	1	< 0.1%
126	1	< 0.1%
141	1	< 0.1%
147	1	< 0.1%
149	1	< 0.1%
150	1	< 0.1%
151	1	< 0.1%
160	1	< 0.1%
179	1	< 0.1%
181	1	< 0.1%

Value	Count	Frequency (%)
4727301	1	< 0.1%
2040534	1	< 0.1%
1827250	1	< 0.1%
1716459	1	< 0.1%
1659146	1	< 0.1%
1626953	1	< 0.1%
1600607	1	< 0.1%
1582826	1	< 0.1%
1574723	1	< 0.1%
1571504	1	< 0.1%

number of tweets
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	37
Distinct (%)	1.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.184051924

Minimum	1
Maximum	47
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	7
95-th percentile	15
Maximum	47
Range	46
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	5.304281683
Coefficient of variation (CV)	1.023192237
Kurtosis	8.729013126
Mean	5.184051924
Median Absolute Deviation (MAD)	2
Skewness	2.414540454
Sum	11182
Variance	28.13540417
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=37)

Value	Count	Frequency (%)
1	534	24.8%
2	339	15.7%
3	219	10.2%
4	200	9.3%
5	163	7.6%
6	137	6.4%
7	95	4.4%
8	69	3.2%
9	61	2.8%
11	57	2.6%
Other values (27)	283	13.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	534	24.8%
2	339	15.7%
3	219	10.2%
4	200	9.3%
5	163	7.6%
6	137	6.4%
7	95	4.4%
8	69	3.2%
9	61	2.8%
10	51	2.4%

Value	Count	Frequency (%)
47	1	< 0.1%
45	1	< 0.1%
42	1	< 0.1%
39	1	< 0.1%
35	1	< 0.1%
32	2	0.1%
31	3	0.1%
30	2	0.1%
29	2	0.1%
28	2	0.1%

price
Real number (ℝ_≥0)

Distinct	2116
Distinct (%)	98.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	201.011601

Minimum	35.79399872
Maximum	891.3800049
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.0 KiB

Quantile statistics

Minimum	35.79399872
5-th percentile	42.28680038
Q1	56.59333293
median	68.78399658
Q3	287
95-th percentile	699.6060059
Maximum	891.3800049
Range	855.5860062
Interquartile range (IQR)	230.4066671

Descriptive statistics

Standard deviation	234.8994779
Coefficient of variation (CV)	1.168586672
Kurtosis	0.5824455492
Mean	201.011601
Median Absolute Deviation (MAD)	20.77399826
Skewness	1.43883195
Sum	433582.0234
Variance	55177.76472
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
855	3	0.1%
72	3	0.1%
59.34799957	2	0.1%
60.79999924	2	0.1%
55.31800079	2	0.1%
65.55599976	2	0.1%
69.98600006	2	0.1%
63.22600174	2	0.1%
63.20000076	2	0.1%
62.2480011	2	0.1%
Other values (2106)	2135	99.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
35.79399872	1	< 0.1%
36.22000122	1	< 0.1%
36.52999878	1	< 0.1%
36.55599976	1	< 0.1%
36.69800186	1	< 0.1%
36.7859993	1	< 0.1%
37.00400162	1	< 0.1%
37.02000046	1	< 0.1%
37.0320015	1	< 0.1%
37.04733404	1	< 0.1%

Value	Count	Frequency (%)
891.3800049	1	< 0.1%
883.0900269	1	< 0.1%
880.0200195	1	< 0.1%
870.3499756	1	< 0.1%
869.6699829	1	< 0.1%
869.4133301	1	< 0.1%
861.4466553	1	< 0.1%
858.0266724	1	< 0.1%
857.0766805	1	< 0.1%
856	1	< 0.1%

percent change
Real number (ℝ)

HIGH CORRELATION

Distinct	2154
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.002025802196

Minimum	-0.1184687551
Maximum	0.1715485869
Zeros	3
Zeros (%)	0.1%
Negative	1008
Negative (%)	46.7%
Memory size	17.0 KiB

Quantile statistics

Minimum	-0.1184687551
5-th percentile	-0.03610361542
Q1	-0.01005215046
median	0.001233700095
Q3	0.01301956268
95-th percentile	0.04023603586
Maximum	0.1715485869
Range	0.290017342
Interquartile range (IQR)	0.02307171313

Descriptive statistics

Standard deviation	0.02582046493
Coefficient of variation (CV)	12.74579768
Kurtosis	5.342198596
Mean	0.002025802196
Median Absolute Deviation (MAD)	0.01157895809
Skewness	0.7017843054
Sum	4.369655336
Variance	0.0006666964092
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3	0.1%
0.001619827649	2	0.1%
0.002318119098	1	< 0.1%
0.01244815905	1	< 0.1%
0.0246936619	1	< 0.1%
-0.02434964286	1	< 0.1%
0.02137085935	1	< 0.1%
-0.01576337742	1	< 0.1%
-0.007116599111	1	< 0.1%
0.03731796443	1	< 0.1%
Other values (2144)	2144	99.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.1184687551	1	< 0.1%
-0.1075722805	1	< 0.1%
-0.1025324905	1	< 0.1%
-0.09879982736	1	< 0.1%
-0.09761302495	1	< 0.1%
-0.09576938373	1	< 0.1%
-0.08972435472	1	< 0.1%
-0.08572444839	1	< 0.1%
-0.08572356895	1	< 0.1%
-0.08410197571	1	< 0.1%

Value	Count	Frequency (%)
0.1715485869	1	< 0.1%
0.1578024935	1	< 0.1%
0.1412863236	1	< 0.1%
0.1351372887	1	< 0.1%
0.1319999695	1	< 0.1%
0.1276189506	1	< 0.1%
0.1210861517	1	< 0.1%
0.1208025545	1	< 0.1%
0.1147155996	1	< 0.1%
0.1123035951	1	< 0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.4 KiB

no change	1723
rise	249
drop	185

Length

Max length	9
Median length	9
Mean length	7.993973111
Min length	4

Characters and Unicode

Total characters	17243
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	no change
2nd row	no change
3rd row	no change
4th row	no change
5th row	drop

Common Values

Value	Count	Frequency (%)
no change	1723	79.9%
rise	249	11.5%
drop	185	8.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	1723	44.4%
change	1723	44.4%
rise	249	6.4%
drop	185	4.8%

Most occurring characters

Value	Count	Frequency (%)
n	3446	20.0%
e	1972	11.4%
o	1908	11.1%
	1723	10.0%
c	1723	10.0%
h	1723	10.0%
a	1723	10.0%
g	1723	10.0%
r	434	2.5%
i	249	1.4%
Other values (3)	619	3.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	15520	90.0%
Space Separator	1723	10.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	3446	22.2%
e	1972	12.7%
o	1908	12.3%
c	1723	11.1%
h	1723	11.1%
a	1723	11.1%
g	1723	11.1%
r	434	2.8%
i	249	1.6%
s	249	1.6%
Other values (2)	370	2.4%

Space Separator

Value	Count	Frequency (%)
	1723	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15520	90.0%
Common	1723	10.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	3446	22.2%
e	1972	12.7%
o	1908	12.3%
c	1723	11.1%
h	1723	11.1%
a	1723	11.1%
g	1723	11.1%
r	434	2.8%
i	249	1.6%
s	249	1.6%
Other values (2)	370	2.4%

Common

Value	Count	Frequency (%)
	1723	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	17243	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	3446	20.0%
e	1972	11.4%
o	1908	11.1%
	1723	10.0%
c	1723	10.0%
h	1723	10.0%
a	1723	10.0%
g	1723	10.0%
r	434	2.5%
i	249	1.4%
Other values (3)	619	3.6%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	date	tweet	username	mentions	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
0	2016-08-23 16:00:00	Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	elonmusk	0	0	872	4640	13965	2	44.967999	0.002318	no change
1	2016-08-28 09:30:00	@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.	elonmusk	0	0	68	137	789	2	44.162666	0.011081	no change
2	2016-08-30 16:00:00	Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY	elonmusk	0	1	142	1839	7353	1	42.268002	-0.022072	no change
3	2016-08-31 16:00:00	@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh	elonmusk	0	0	391	2021	10165	5	42.402000	0.007508	no change
4	2016-09-01 16:00:00	Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.	elonmusk	0	0	1159	4848	10743	1	40.153999	-0.039424	drop
5	2016-09-02 09:30:00	@scrappydog yes. This seems instant from a human perspective, but it really a fast fire, not an explosion. Dragon would have been fine. Finishing Autopilot blog postponed to end of weekend	elonmusk	0	0	426	588	3702	2	40.466000	0.007770	no change
6	2016-09-09 09:30:00	Will get back to Autopilot update blog tomorrow. @ashwin7002 @NASA @faa @AFPAA We have not ruled that out. @LewisChandlerDN nope, it wasn't me Particularly trying to understand the quieter bang sound a few seconds before the fireball goes off. May come from rocket or something else. Support & advice from @NASA, @FAA, @AFPAA & others much appreciated. Please email any recordings of the event to report@spacex.com. Important to note that this happened during a routine filling operation. Engines were not on and there was no apparent heat source. Still working on the Falcon fireball investigation. Turning out to be the most difficult and complex failure we have ever had in 14 years. @waitbutwhy It's been a little crazy lately	elonmusk	3	0	1761	5752	20453	8	39.818001	0.008766	no change
7	2016-09-09 16:00:00	@abadcliche Most likely true, but we can't yet find it on any vehicle sensors	elonmusk	1	0	31	23	189	1	38.894001	-0.023206	no change
8	2016-09-10 09:30:00	Thoughtful Op-ed in Space News much appreciated https://t.co/CJq5g3NIEK	elonmusk	0	1	138	1400	3698	1	39.545334	0.016746	no change
9	2016-09-10 16:00:00	Will do some press Q&A on Autopilot post at 11am PDT tmrw and then publish at noon. Sorry about delay. Unusually difficult couple of weeks.	elonmusk	0	0	321	845	5376	1	39.149334	-0.010014	no change

Last rows

	date	tweet	username	mentions	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
2147	2021-07-15 16:00:00	@Erdayastronaut @Model3Owners How about a wifi camera link? @BLKMDL3 @Model3Owners In end, we kept production design almost exactly same as show car. Just some small tweaks here & there to make it slightly better. No door handles. Car recognizes you & opens door. Having all four wheels steer is amazing for nimble handling & tight turns! @johnkrausphotos @SpaceX @PortCanaveral Version 3 of the SpaceX droneship. Team did great work! Will be epic to see the deep sea oil rigs converted to ocean spaceports for Starship. @Model3Owners To be frank, there is always some chance that Cybertruck will flop, because it is so unlike anything else. I don’t care. I love it so much even if others don’t. Other trucks look like copies of the same thing, but Cybertruck looks like it was made by aliens from the future. @TesLatino @klwtts @jpr007 Tapering down charge rate is simply a physical thing that has to happen, as lithium ions bounce around what is an increasingly full “parking lot”. Just like a car parking lot, where it takes longer to find a spot when the lot is almost full.	elonmusk	0	0	0	0	5420	4030	57028	5	650.599976	-0.011832	no change
2148	2021-07-16 09:30:00	@AustinTeslaClub @SpaceX @austinbarnard45 @PPathole @TeslaOwnersEBay @bluemoondance74 @teslaownersSV @JohnnaCrider1 @TeslaNY Absolutely! @Erdayastronaut @SpaceX Probably Monday @AaronS5_ @FrenchieEAP @karpathy Yes @FrenchieEAP @karpathy FSD beta 9 is using the pure vision production code for highway driving. Beta 10 hopefully (Beta 11 definitely) will use one stack to rule them all – city streets, highway & complex parking lots.	elonmusk	0	0	0	0	2851	1771	40018	4	654.679993	0.006271	no change
2149	2021-07-16 16:00:00	@Teslarati Improving permit approval speed & lowering permit costs for solar would make a big difference @TeslaNY Do you even press? @enn_nafnlaus @jpr007 @TesLatino @klwtts Indeed, but again like a parking lot, a battery having big “roads” tends to decrease number of “parking spaces” (ie stores less energy) @RationalEtienne @etherkragg Those are major factors	elonmusk	0	0	0	0	2236	1346	25484	4	644.219971	-0.015977	no change
2150	2021-07-17 09:30:00	@techAU Roughly @JeffTutorials @TonyTesla4Life @WholeMarsBlog Yes @TonyTesla4Life @WholeMarsBlog Wide beta maybe with FSD rev 10, definitely with rev 11 @fael097 Pure coincidence! @ValaAfshar Even smaller to a @neuralink chip @facebookai To date, AI chatbots have had a rather short MtH (meantime to Hitler) score. Tay was ~16 hours. https://t.co/FnWMXgpZji @ErcXspace @NASASpaceflight @SpaceX Some of these design trades are still open, but will be resolved soon @ErcXspace @NASASpaceflight @SpaceX Very accurate!	elonmusk	1	0	0	1	5382	3775	59586	8	646.416667	0.003410	no change
2151	2021-07-17 16:00:00	@billycrammer @Tesla Cool! @engineers_feed There’s a corner case where brick density is same density as water, reaching bottom due to momentum Fred Astaire is incredible. Worth watching his movies. One of a kind. @TrungTPhan Now, he can bench press a rhino @SamTwits Nice Tap on the screen https://t.co/YPyyj8V8DF @AshleyIllusion1 @lexfridman Lil X is hodling his Doge like a champ. Literally never said the word “sell” even once! @lexfridman “All your basis points are belong to us” - fiat issuers @engineers_feed A classic @riorahardi618 True https://t.co/d4ZOSKZESP	elonmusk	0	2	2	0	31886	30257	451470	11	644.886637	-0.002367	no change
2152	2021-07-18 09:30:00	Cybrrrtruck https://t.co/rdiMFdYOS6 @ArtifactsHub And all-time hodl champion @ValaAfshar Indeed @waitbutwhy Pohtaytohz @squawksquare Current Summon is sometimes useful, but mostly just a fun trick. Once we move summon (plus highway driving) to a single FSD stack, it will be sublime.	elonmusk	0	1	0	0	18606	24221	291423	5	638.153341	-0.010441	no change
2153	2021-07-18 16:00:00	@thePiggsBoson Problem 1st, theory 2nd is for sure way to go, as it establishes relevance, thus improving memory retention	elonmusk	0	0	0	0	475	252	2873	1	645.553304	0.011596	no change
2154	2021-07-19 09:30:00	@DragTimes @Tesla Nice @WholeMarsBlog You don’t even need to touch the shifter in new S. Auto detect direction will come as an optional setting to all cars with FSD.	elonmusk	0	0	0	0	1257	819	19337	2	629.890015	-0.024263	no change
2155	2021-07-19 16:00:00	@jack @BitcoinMagazine @CathieDWood Sure, I have a ton @BitcoinMagazine @jack @CathieDWood During this talk, we will sing a cover of The Final Countdown by Europe https://t.co/7YUXiW8dhd	elonmusk	0	0	0	1	1953	1477	22616	2	646.219971	0.025925	rise
2156	2021-07-20 09:30:00	@vincent13031925 Great to hear! @blueorigin Best of luck tomorrow! @TLPN_Official @SpaceX Depending on progress with Booster 4, we might try a 9 engine firing on Booster 3 Full test duration firing of 3 Raptors on Super Heavy Booster!	elonmusk	0	0	0	0	7024	7245	145057	4	651.989990	0.008929	no change

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Other Symbol

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Math Symbol

Other Letter

Open Punctuation

Close Punctuation

Dash Punctuation

Currency Symbol

Initial Punctuation

Final Punctuation

Format

Modifier Symbol

Space Separator

Connector Punctuation

Nonspacing Mark

Most occurring scripts

Most frequent character per script

Common

Latin

Cyrillic

Han

Inherited

Greek

Bamum

Most occurring blocks

Most frequent character per block

ASCII

Punctuation

Dingbats

VS

Emoticons

None

Latin 1 Sup

Enclosed Alphanum Sup

Misc Symbols

Currency Symbols

CJK

Latin Ext A

Math Operators

Letterlike Symbols

Cyrillic

Bamum Sup

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length